Estimating Value at Risk: From JP Morgan's Standard-EWMA to Skewed-EWMA Forecasting

Lu, Zudi; Huang, Hai

doi:10.2139/ssrn.1009022

Cited by 2 publications

(5 citation statements)

References 26 publications

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“…Guermat and Harris (2001) introduce robust EWMA VaR estimator that is based on the maximum likelihood estimator of the standard deviation of the Laplace distribution, and it is a function of an exponentially weighted moving average of the absolute value of past returns, rather than their squares. Later Lu et al (2010) derived skewed-EWMA VaR estimator that has taken into account both skewness and heavy tails in return distribution and also their timevarying nature in practice. In this paper we compare standard EWMA, robust and skewed EWMA VaR estimators.…”

Section: Literature Reviewmentioning

confidence: 99%

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VaR BASED RISK MANAGEMENT

Bohdalová¹,

Greguš²

2013

CBU International Conference Proceedings

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In this paper we discuss the Value-at-Risk concept and we analyse the market risk by using EWMA approach. EWMA (exponentially weighted moving average) forecasting technique is a popular measure of various risks in financial risk management. We will compare standard EWMA, robust EWMA and skewed EWMA forecast of VaR. JP Morgan standard EWMA is derived from Gaussian distribution. Robust EWMA is based on Laplace distribution and skewed EWMA is a new approach derived from an asymmetric Laplace distribution. Asymmetric Laplace distribution takes into account both skewness and heavy tails in return distribution and the time varying nature of them in practice. Skewed EWMA VaR is a generalization of the standard EWMA method. Using these approaches we will analyse selected financial series (three European market indexes and one exchange rate). We have found and confirmed that skewed EWMA forecasting of VaR outperforms the standard EWMA method.

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Section: Literature Reviewmentioning

confidence: 99%

“…9). As it is written in (Lu, Huang & Gerlach, 2010), this skewed-EWMA estimate of standard deviation ( 11) is a special first order threshold GARCH (TGARCH) model.…”

Section: Skewed Ewma Based Var Modellingmentioning

confidence: 99%

VaR BASED RISK MANAGEMENT

Bohdalová¹,

Greguš²

2013

CBU International Conference Proceedings

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show abstract

Section: Literature Reviewmentioning

confidence: 99%

“…Skewed EWMA VaR model was developed by (Lu, Huang & Gerlach, 2010). This estimator is based on asymmetric Laplace distribution to take into account both skewness and heavy tails in financial return distribution and it generalizes the robust EWMA VaR estimator as special case.…”

Section: Skewed Ewma Based Var Modellingmentioning

confidence: 99%

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VaR BASED RISK MANAGEMENT

Bohdalová

Greguš

2013

CBUP

View full text Add to dashboard Cite

In this paper we discuss the Value–at–Risk concept and we analyse the market risk by using EWMA approach. EWMA (exponentially weighted moving average) forecasting technique is a popular measure of various risks in financial risk management. We will compare standard EWMA, robust EWMA and skewed EWMA forecast of VaR. JP Morgan standard EWMA is derived from Gaussian distribution. Robust EWMA is based on Laplace distribution and skewed EWMA is a new approach derived from an asymmetric Laplace distribution. Asymmetric Laplace distribution takes into account both skewness and heavy tails in return distribution and the time varying nature of them in practice. Skewed EWMA VaR is a generalization of the standard EWMA method. Using these approaches we will analyse selected financial series (three European market indexes and one exchange rate). We have found andconfirmed that skewed EWMA forecasting of VaR outperforms the standard EWMA method.

show abstract

Estimating Value at Risk: From JP Morgan's Standard-EWMA to Skewed-EWMA Forecasting

Cited by 2 publications

References 26 publications

VaR BASED RISK MANAGEMENT

VaR BASED RISK MANAGEMENT

VaR BASED RISK MANAGEMENT

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